PROJECT SUMMARY In response to the emergence of multi-drug-resistant Plasmodium falciparum in the Greater Mekong Subregion, the World Health Organization is working with local partners to completely eliminate malaria from this geographic region by 2030. Elimination efforts in the region have led to drastic reductions in the number of malaria cases and deaths. However, elimination will become increasingly difficult to achieve as the species composition shifts from P. falciparum to P. vivax (the more difficult species to eliminate), and the malaria burden becomes more concentrated in border areas, where frequent movement of human populations and mosquito vectors across borders and the difficulties of conducting surveillance and allocating resources between different countries make elimination challenging. Local information about factors driving malaria risk will be important for prioritizing resources and optimizing strategies for malaria elimination, particularly in border areas. Estimates of parasite migration are important in stratifying malaria risk. Population genomics approaches are beginning to be used to understand connectivity between parasite populations; however, many of these studies have focused primarily on regional geographic scales and/or have only used geospatial data to make post hoc geographic interpretations. Here, we propose an approach that explicitly models the spatial structure in genomic data to understand parasite migration patterns in an area of emerging drug resistance along the northern border of Cambodia with Thailand. The work will be accomplished in two aims. First, we will estimate the local population structure and migration of P. falciparum and P. vivax in an area of dense sampling on either side of the northern border of Cambodia with Thailand. To achieve this aim, we will generate whole-genome sequence data for P. falciparum and P. vivax and utilize estimated effective migration surfaces (EEMS) based on rare variation and identity-by-descent to infer connectivity of P. falciparum and P. vivax populations between different study sites. Second, we will estimate local human travel patterns and their association with the parasite migration contours from Aim 1. To achieve this aim, we will develop a model of local travel networks that is spatially and temporally explicit at the village level and that accounts for key geospatial features in the region that impact human movement and effective migration. The association between estimated local human travel patterns and parasite migration patterns will be assessed and will facilitate identification of segments of the travel network that coincide with regions of high parasite migration that can be used to define geographical units for targeting elimination interventions. If successful, the proposed research will illuminate the contribution of movement by local population groups to spatial patterns of parasite migration and will provide a framework to identify specific geographic areas for targeted intervention, which can be adapted to other malaria-endemic areas with intermediate levels of transmission.